Regular expressions (called REs, or regexes, or regex patterns) are essentially
a tiny, highly specialized programming language embedded inside Python and made
available through the re module. Using this little language, you specify
the rules for the set of possible strings that you want to match; this set might
contain English sentences, or e-mail addresses, or TeX commands, or anything you
like. You can then ask questions such as “Does this string match the pattern?”,
or “Is there a match for the pattern anywhere in this string?”. You can also
use REs to modify a string or to split it apart in various ways.

Regular expression patterns are compiled into a series of bytecodes which are
then executed by a matching engine written in C. For advanced use, it may be
necessary to pay careful attention to how the engine will execute a given RE,
and write the RE in a certain way in order to produce bytecode that runs faster.
Optimization isn’t covered in this document, because it requires that you have a
good understanding of the matching engine’s internals.

The regular expression language is relatively small and restricted, so not all
possible string processing tasks can be done using regular expressions. There
are also tasks that can be done with regular expressions, but the expressions
turn out to be very complicated. In these cases, you may be better off writing
Python code to do the processing; while Python code will be slower than an
elaborate regular expression, it will also probably be more understandable.

Most letters and characters will simply match themselves. For example, the
regular expression test will match the string test exactly. (You can
enable a case-insensitive mode that would let this RE match Test or TEST
as well; more about this later.)

There are exceptions to this rule; some characters are special
metacharacters, and don’t match themselves. Instead, they signal that
some out-of-the-ordinary thing should be matched, or they affect other portions
of the RE by repeating them or changing their meaning. Much of this document is
devoted to discussing various metacharacters and what they do.

Here’s a complete list of the metacharacters; their meanings will be discussed
in the rest of this HOWTO.

. ^ $ * + ? { } [ ] \ | ( )

The first metacharacters we’ll look at are [ and ]. They’re used for
specifying a character class, which is a set of characters that you wish to
match. Characters can be listed individually, or a range of characters can be
indicated by giving two characters and separating them by a '-'. For
example, [abc] will match any of the characters a, b, or c; this
is the same as [a-c], which uses a range to express the same set of
characters. If you wanted to match only lowercase letters, your RE would be
[a-z].

Metacharacters are not active inside classes. For example, [akm$] will
match any of the characters 'a', 'k', 'm', or '$'; '$' is
usually a metacharacter, but inside a character class it’s stripped of its
special nature.

You can match the characters not listed within the class by complementing
the set. This is indicated by including a '^' as the first character of the
class; '^' outside a character class will simply match the '^'
character. For example, [^5] will match any character except '5'.

Perhaps the most important metacharacter is the backslash, \. As in Python
string literals, the backslash can be followed by various characters to signal
various special sequences. It’s also used to escape all the metacharacters so
you can still match them in patterns; for example, if you need to match a [
or \, you can precede them with a backslash to remove their special
meaning: \[ or \\.

Some of the special sequences beginning with '\' represent
predefined sets of characters that are often useful, such as the set
of digits, the set of letters, or the set of anything that isn’t
whitespace.

Let’s take an example: \w matches any alphanumeric character. If
the regex pattern is expressed in bytes, this is equivalent to the
class [a-zA-Z0-9_]. If the regex pattern is a string, \w will
match all the characters marked as letters in the Unicode database
provided by the unicodedata module. You can use the more
restricted definition of \w in a string pattern by supplying the
re.ASCII flag when compiling the regular expression.

The following list of special sequences isn’t complete. For a complete
list of sequences and expanded class definitions for Unicode string
patterns, see the last part of Regular Expression Syntax in the Standard Library reference. In general, the
Unicode versions match any character that’s in the appropriate
category in the Unicode database.

\d

Matches any decimal digit; this is equivalent to the class [0-9].

\D

Matches any non-digit character; this is equivalent to the class [^0-9].

\s

Matches any whitespace character; this is equivalent to the class [\t\n\r\f\v].

\S

Matches any non-whitespace character; this is equivalent to the class [^\t\n\r\f\v].

\w

Matches any alphanumeric character; this is equivalent to the class
[a-zA-Z0-9_].

\W

Matches any non-alphanumeric character; this is equivalent to the class
[^a-zA-Z0-9_].

These sequences can be included inside a character class. For example,
[\s,.] is a character class that will match any whitespace character, or
',' or '.'.

The final metacharacter in this section is .. It matches anything except a
newline character, and there’s an alternate mode (re.DOTALL) where it will
match even a newline. '.' is often used where you want to match “any
character”.

Being able to match varying sets of characters is the first thing regular
expressions can do that isn’t already possible with the methods available on
strings. However, if that was the only additional capability of regexes, they
wouldn’t be much of an advance. Another capability is that you can specify that
portions of the RE must be repeated a certain number of times.

The first metacharacter for repeating things that we’ll look at is *. *
doesn’t match the literal character *; instead, it specifies that the
previous character can be matched zero or more times, instead of exactly once.

For example, ca*t will match ct (0 a characters), cat (1 a),
caaat (3 a characters), and so forth. The RE engine has various
internal limitations stemming from the size of C’s int type that will
prevent it from matching over 2 billion a characters; patterns
are usually not written to match that much data.

Repetitions such as * are greedy; when repeating a RE, the matching
engine will try to repeat it as many times as possible. If later portions of the
pattern don’t match, the matching engine will then back up and try again with
few repetitions.

A step-by-step example will make this more obvious. Let’s consider the
expression a[bcd]*b. This matches the letter 'a', zero or more letters
from the class [bcd], and finally ends with a 'b'. Now imagine matching
this RE against the string abcbd.

Step

Matched

Explanation

1

a

The a in the RE matches.

2

abcbd

The engine matches [bcd]*,
going as far as it can, which
is to the end of the string.

3

Failure

The engine tries to match
b, but the current position
is at the end of the string, so
it fails.

4

abcb

Back up, so that [bcd]*
matches one less character.

5

Failure

Try b again, but the
current position is at the last
character, which is a 'd'.

6

abc

Back up again, so that
[bcd]* is only matching
bc.

6

abcb

Try b again. This time
the character at the
current position is 'b', so
it succeeds.

The end of the RE has now been reached, and it has matched abcb. This
demonstrates how the matching engine goes as far as it can at first, and if no
match is found it will then progressively back up and retry the rest of the RE
again and again. It will back up until it has tried zero matches for
[bcd]*, and if that subsequently fails, the engine will conclude that the
string doesn’t match the RE at all.

Another repeating metacharacter is +, which matches one or more times. Pay
careful attention to the difference between * and +; * matches
zero or more times, so whatever’s being repeated may not be present at all,
while + requires at least one occurrence. To use a similar example,
ca+t will match cat (1 a), caaat (3 a‘s), but won’t match
ct.

There are two more repeating qualifiers. The question mark character, ?,
matches either once or zero times; you can think of it as marking something as
being optional. For example, home-?brew matches either homebrew or
home-brew.

The most complicated repeated qualifier is {m,n}, where m and n are
decimal integers. This qualifier means there must be at least m repetitions,
and at most n. For example, a/{1,3}b will match a/b, a//b, and
a///b. It won’t match ab, which has no slashes, or a////b, which
has four.

You can omit either m or n; in that case, a reasonable value is assumed for
the missing value. Omitting m is interpreted as a lower limit of 0, while
omitting n results in an upper bound of infinity — actually, the upper bound
is the 2-billion limit mentioned earlier, but that might as well be infinity.

Readers of a reductionist bent may notice that the three other qualifiers can
all be expressed using this notation. {0,} is the same as *, {1,}
is equivalent to +, and {0,1} is the same as ?. It’s better to use
*, +, or ? when you can, simply because they’re shorter and easier
to read.

Now that we’ve looked at some simple regular expressions, how do we actually use
them in Python? The re module provides an interface to the regular
expression engine, allowing you to compile REs into objects and then perform
matches with them.

re.compile() also accepts an optional flags argument, used to enable
various special features and syntax variations. We’ll go over the available
settings later, but for now a single example will do:

>>> p=re.compile('ab*',re.IGNORECASE)

The RE is passed to re.compile() as a string. REs are handled as strings
because regular expressions aren’t part of the core Python language, and no
special syntax was created for expressing them. (There are applications that
don’t need REs at all, so there’s no need to bloat the language specification by
including them.) Instead, the re module is simply a C extension module
included with Python, just like the socket or zlib modules.

Putting REs in strings keeps the Python language simpler, but has one
disadvantage which is the topic of the next section.

As stated earlier, regular expressions use the backslash character ('\') to
indicate special forms or to allow special characters to be used without
invoking their special meaning. This conflicts with Python’s usage of the same
character for the same purpose in string literals.

Let’s say you want to write a RE that matches the string \section, which
might be found in a LaTeX file. To figure out what to write in the program
code, start with the desired string to be matched. Next, you must escape any
backslashes and other metacharacters by preceding them with a backslash,
resulting in the string \\section. The resulting string that must be passed
to re.compile() must be \\section. However, to express this as a
Python string literal, both backslashes must be escaped again.

In short, to match a literal backslash, one has to write '\\\\' as the RE
string, because the regular expression must be \\, and each backslash must
be expressed as \\ inside a regular Python string literal. In REs that
feature backslashes repeatedly, this leads to lots of repeated backslashes and
makes the resulting strings difficult to understand.

The solution is to use Python’s raw string notation for regular expressions;
backslashes are not handled in any special way in a string literal prefixed with
'r', so r"\n" is a two-character string containing '\' and 'n',
while "\n" is a one-character string containing a newline. Regular
expressions will often be written in Python code using this raw string notation.

Once you have an object representing a compiled regular expression, what do you
do with it? Pattern objects have several methods and attributes.
Only the most significant ones will be covered here; consult the re docs
for a complete listing.

Method/Attribute

Purpose

match()

Determine if the RE matches at the beginning
of the string.

search()

Scan through a string, looking for any
location where this RE matches.

findall()

Find all substrings where the RE matches, and
returns them as a list.

finditer()

Find all substrings where the RE matches, and
returns them as an iterator.

match() and search() return None if no match can be found. If
they’re successful, a match object instance is returned,
containing information about the match: where it starts and ends, the substring
it matched, and more.

You can learn about this by interactively experimenting with the re
module. If you have tkinter available, you may also want to look at
Tools/demo/redemo.py, a demonstration program included with the
Python distribution. It allows you to enter REs and strings, and displays
whether the RE matches or fails. redemo.py can be quite useful when
trying to debug a complicated RE. Phil Schwartz’s Kodos is also an interactive tool for developing and
testing RE patterns.

This HOWTO uses the standard Python interpreter for its examples. First, run the
Python interpreter, import the re module, and compile a RE:

Now, you can try matching various strings against the RE [a-z]+. An empty
string shouldn’t match at all, since + means ‘one or more repetitions’.
match() should return None in this case, which will cause the
interpreter to print no output. You can explicitly print the result of
match() to make this clear.

>>> p.match("")>>> print(p.match(""))None

Now, let’s try it on a string that it should match, such as tempo. In this
case, match() will return a match object, so you
should store the result in a variable for later use.

>>> m=p.match('tempo')>>> m<_sre.SRE_Match object at 0x...>

Now you can query the match object for information
about the matching string. match object instances
also have several methods and attributes; the most important ones are:

Method/Attribute

Purpose

group()

Return the string matched by the RE

start()

Return the starting position of the match

end()

Return the ending position of the match

span()

Return a tuple containing the (start, end)
positions of the match

Trying these methods will soon clarify their meaning:

>>> m.group()'tempo'>>> m.start(),m.end()(0, 5)>>> m.span()(0, 5)

group() returns the substring that was matched by the RE. start()
and end() return the starting and ending index of the match. span()
returns both start and end indexes in a single tuple. Since the match()
method only checks if the RE matches at the start of a string, start()
will always be zero. However, the search() method of patterns
scans through the string, so the match may not start at zero in that
case.

You don’t have to create a pattern object and call its methods; the
re module also provides top-level functions called match(),
search(), findall(), sub(), and so forth. These functions
take the same arguments as the corresponding pattern method with
the RE string added as the first argument, and still return either None or a
match object instance.

Under the hood, these functions simply create a pattern object for you
and call the appropriate method on it. They also store the compiled
object in a cache, so future calls using the same RE won’t need to
parse the pattern again and again.

Should you use these module-level functions, or should you get the
pattern and call its methods yourself? If you’re accessing a regex
within a loop, pre-compiling it will save a few function calls.
Outside of loops, there’s not much difference thanks to the internal
cache.

Compilation flags let you modify some aspects of how regular expressions work.
Flags are available in the re module under two names, a long name such as
IGNORECASE and a short, one-letter form such as I. (If you’re
familiar with Perl’s pattern modifiers, the one-letter forms use the same
letters; the short form of re.VERBOSE is re.X, for example.)
Multiple flags can be specified by bitwise OR-ing them; re.I|re.M sets
both the I and M flags, for example.

Here’s a table of the available flags, followed by a more detailed explanation
of each one.

Flag

Meaning

ASCII, A

Makes several escapes like \w, \b,
\s and \d match only on ASCII
characters with the respective property.

DOTALL, S

Make . match any character, including
newlines

IGNORECASE, I

Do case-insensitive matches

LOCALE, L

Do a locale-aware match

MULTILINE, M

Multi-line matching, affecting ^ and
$

VERBOSE, X
(for ‘extended’)

Enable verbose REs, which can be organized
more cleanly and understandably.

I

IGNORECASE

Perform case-insensitive matching; character class and literal strings will
match letters by ignoring case. For example, [A-Z] will match lowercase
letters, too, and Spam will match Spam, spam, or spAM. This
lowercasing doesn’t take the current locale into account; it will if you also
set the LOCALE flag.

L

LOCALE

Make \w, \W, \b, and \B, dependent on the current locale
instead of the Unicode database.

Locales are a feature of the C library intended to help in writing programs that
take account of language differences. For example, if you’re processing French
text, you’d want to be able to write \w+ to match words, but \w only
matches the character class [A-Za-z]; it won’t match 'é' or 'ç'. If
your system is configured properly and a French locale is selected, certain C
functions will tell the program that 'é' should also be considered a letter.
Setting the LOCALE flag when compiling a regular expression will cause
the resulting compiled object to use these C functions for \w; this is
slower, but also enables \w+ to match French words as you’d expect.

Usually ^ matches only at the beginning of the string, and $ matches
only at the end of the string and immediately before the newline (if any) at the
end of the string. When this flag is specified, ^ matches at the beginning
of the string and at the beginning of each line within the string, immediately
following each newline. Similarly, the $ metacharacter matches either at
the end of the string and at the end of each line (immediately preceding each
newline).

S

DOTALL

Makes the '.' special character match any character at all, including a
newline; without this flag, '.' will match anything except a newline.

A

ASCII

Make \w, \W, \b, \B, \s and \S perform ASCII-only
matching instead of full Unicode matching. This is only meaningful for
Unicode patterns, and is ignored for byte patterns.

X

VERBOSE

This flag allows you to write regular expressions that are more readable by
granting you more flexibility in how you can format them. When this flag has
been specified, whitespace within the RE string is ignored, except when the
whitespace is in a character class or preceded by an unescaped backslash; this
lets you organize and indent the RE more clearly. This flag also lets you put
comments within a RE that will be ignored by the engine; comments are marked by
a '#' that’s neither in a character class or preceded by an unescaped
backslash.

For example, here’s a RE that uses re.VERBOSE; see how much easier it
is to read?

In the above example, Python’s automatic concatenation of string literals has
been used to break up the RE into smaller pieces, but it’s still more difficult
to understand than the version using re.VERBOSE.

There are some metacharacters that we haven’t covered yet. Most of them will be
covered in this section.

Some of the remaining metacharacters to be discussed are zero-width
assertions. They don’t cause the engine to advance through the string;
instead, they consume no characters at all, and simply succeed or fail. For
example, \b is an assertion that the current position is located at a word
boundary; the position isn’t changed by the \b at all. This means that
zero-width assertions should never be repeated, because if they match once at a
given location, they can obviously be matched an infinite number of times.

|

Alternation, or the “or” operator. If A and B are regular expressions,
A|B will match any string that matches either A or B. | has very
low precedence in order to make it work reasonably when you’re alternating
multi-character strings. Crow|Servo will match either Crow or Servo,
not Cro, a 'w' or an 'S', and ervo.

To match a literal '|', use \|, or enclose it inside a character class,
as in [|].

^

Matches at the beginning of lines. Unless the MULTILINE flag has been
set, this will only match at the beginning of the string. In MULTILINE
mode, this also matches immediately after each newline within the string.

For example, if you wish to match the word From only at the beginning of a
line, the RE to use is ^From.

To match a literal '$', use \$ or enclose it inside a character class,
as in [$].

\A

Matches only at the start of the string. When not in MULTILINE mode,
\A and ^ are effectively the same. In MULTILINE mode, they’re
different: \A still matches only at the beginning of the string, but ^
may match at any location inside the string that follows a newline character.

\Z

Matches only at the end of the string.

\b

Word boundary. This is a zero-width assertion that matches only at the
beginning or end of a word. A word is defined as a sequence of alphanumeric
characters, so the end of a word is indicated by whitespace or a
non-alphanumeric character.

The following example matches class only when it’s a complete word; it won’t
match when it’s contained inside another word.

There are two subtleties you should remember when using this special sequence.
First, this is the worst collision between Python’s string literals and regular
expression sequences. In Python’s string literals, \b is the backspace
character, ASCII value 8. If you’re not using raw strings, then Python will
convert the \b to a backspace, and your RE won’t match as you expect it to.
The following example looks the same as our previous RE, but omits the 'r'
in front of the RE string.

Frequently you need to obtain more information than just whether the RE matched
or not. Regular expressions are often used to dissect strings by writing a RE
divided into several subgroups which match different components of interest.
For example, an RFC-822 header line is divided into a header name and a value,
separated by a ':', like this:

This can be handled by writing a regular expression which matches an entire
header line, and has one group which matches the header name, and another group
which matches the header’s value.

Groups are marked by the '(', ')' metacharacters. '(' and ')'
have much the same meaning as they do in mathematical expressions; they group
together the expressions contained inside them, and you can repeat the contents
of a group with a repeating qualifier, such as *, +, ?, or
{m,n}. For example, (ab)* will match zero or more repetitions of
ab.

Groups indicated with '(', ')' also capture the starting and ending
index of the text that they match; this can be retrieved by passing an argument
to group(), start(), end(), and span(). Groups are
numbered starting with 0. Group 0 is always present; it’s the whole RE, so
match object methods all have group 0 as their default
argument. Later we’ll see how to express groups that don’t capture the span
of text that they match.

group() can be passed multiple group numbers at a time, in which case it
will return a tuple containing the corresponding values for those groups.

>>> m.group(2,1,2)('b', 'abc', 'b')

The groups() method returns a tuple containing the strings for all the
subgroups, from 1 up to however many there are.

>>> m.groups()('abc', 'b')

Backreferences in a pattern allow you to specify that the contents of an earlier
capturing group must also be found at the current location in the string. For
example, \1 will succeed if the exact contents of group 1 can be found at
the current position, and fails otherwise. Remember that Python’s string
literals also use a backslash followed by numbers to allow including arbitrary
characters in a string, so be sure to use a raw string when incorporating
backreferences in a RE.

For example, the following RE detects doubled words in a string.

>>> p=re.compile(r'(\b\w+)\s+\1')>>> p.search('Paris in the the spring').group()'the the'

Backreferences like this aren’t often useful for just searching through a string
— there are few text formats which repeat data in this way — but you’ll soon
find out that they’re very useful when performing string substitutions.

Elaborate REs may use many groups, both to capture substrings of interest, and
to group and structure the RE itself. In complex REs, it becomes difficult to
keep track of the group numbers. There are two features which help with this
problem. Both of them use a common syntax for regular expression extensions, so
we’ll look at that first.

Perl 5 is well-known for its powerful additions to standard regular expressions.
For these new features the Perl developers couldn’t choose new single-keystroke metacharacters
or new special sequences beginning with \ without making Perl’s regular
expressions confusingly different from standard REs. If they chose & as a
new metacharacter, for example, old expressions would be assuming that & was
a regular character and wouldn’t have escaped it by writing \& or [&].

The solution chosen by the Perl developers was to use (?...) as the
extension syntax. ? immediately after a parenthesis was a syntax error
because the ? would have nothing to repeat, so this didn’t introduce any
compatibility problems. The characters immediately after the ? indicate
what extension is being used, so (?=foo) is one thing (a positive lookahead
assertion) and (?:foo) is something else (a non-capturing group containing
the subexpression foo).

Python supports several of Perl’s extensions and adds an extension
syntax to Perl’s extension syntax. If the first character after the
question mark is a P, you know that it’s an extension that’s
specific to Python.

Now that we’ve looked at the general extension syntax, we can return
to the features that simplify working with groups in complex REs.

Sometimes you’ll want to use a group to denote a part of a regular expression,
but aren’t interested in retrieving the group’s contents. You can make this fact
explicit by using a non-capturing group: (?:...), where you can replace the
... with any other regular expression.

Except for the fact that you can’t retrieve the contents of what the group
matched, a non-capturing group behaves exactly the same as a capturing group;
you can put anything inside it, repeat it with a repetition metacharacter such
as *, and nest it within other groups (capturing or non-capturing).
(?:...) is particularly useful when modifying an existing pattern, since you
can add new groups without changing how all the other groups are numbered. It
should be mentioned that there’s no performance difference in searching between
capturing and non-capturing groups; neither form is any faster than the other.

A more significant feature is named groups: instead of referring to them by
numbers, groups can be referenced by a name.

The syntax for a named group is one of the Python-specific extensions:
(?P<name>...). name is, obviously, the name of the group. Named groups
behave exactly like capturing groups, and additionally associate a name
with a group. The match object methods that deal with
capturing groups all accept either integers that refer to the group by number
or strings that contain the desired group’s name. Named groups are still
given numbers, so you can retrieve information about a group in two ways:

It’s obviously much easier to retrieve m.group('zonem'), instead of having
to remember to retrieve group 9.

The syntax for backreferences in an expression such as (...)\1 refers to the
number of the group. There’s naturally a variant that uses the group name
instead of the number. This is another Python extension: (?P=name) indicates
that the contents of the group called name should again be matched at the
current point. The regular expression for finding doubled words,
(\b\w+)\s+\1 can also be written as (?P<word>\b\w+)\s+(?P=word):

>>> p=re.compile(r'(?P<word>\b\w+)\s+(?P=word)')>>> p.search('Paris in the the spring').group()'the the'

Another zero-width assertion is the lookahead assertion. Lookahead assertions
are available in both positive and negative form, and look like this:

(?=...)

Positive lookahead assertion. This succeeds if the contained regular
expression, represented here by ..., successfully matches at the current
location, and fails otherwise. But, once the contained expression has been
tried, the matching engine doesn’t advance at all; the rest of the pattern is
tried right where the assertion started.

(?!...)

Negative lookahead assertion. This is the opposite of the positive assertion;
it succeeds if the contained expression doesn’t match at the current position
in the string.

To make this concrete, let’s look at a case where a lookahead is useful.
Consider a simple pattern to match a filename and split it apart into a base
name and an extension, separated by a .. For example, in news.rc,
news is the base name, and rc is the filename’s extension.

The pattern to match this is quite simple:

.*[.].*$

Notice that the . needs to be treated specially because it’s a
metacharacter, so it’s inside a character class to only match that
specific character. Also notice the trailing $; this is added to
ensure that all the rest of the string must be included in the
extension. This regular expression matches foo.bar and
autoexec.bat and sendmail.cf and printers.conf.

Now, consider complicating the problem a bit; what if you want to match
filenames where the extension is not bat? Some incorrect attempts:

.*[.][^b].*$ The first attempt above tries to exclude bat by requiring
that the first character of the extension is not a b. This is wrong,
because the pattern also doesn’t match foo.bar.

.*[.]([^b]..|.[^a].|..[^t])$

The expression gets messier when you try to patch up the first solution by
requiring one of the following cases to match: the first character of the
extension isn’t b; the second character isn’t a; or the third character
isn’t t. This accepts foo.bar and rejects autoexec.bat, but it
requires a three-letter extension and won’t accept a filename with a two-letter
extension such as sendmail.cf. We’ll complicate the pattern again in an
effort to fix it.

.*[.]([^b].?.?|.[^a]?.?|..?[^t]?)$

In the third attempt, the second and third letters are all made optional in
order to allow matching extensions shorter than three characters, such as
sendmail.cf.

The pattern’s getting really complicated now, which makes it hard to read and
understand. Worse, if the problem changes and you want to exclude both bat
and exe as extensions, the pattern would get even more complicated and
confusing.

A negative lookahead cuts through all this confusion:

.*[.](?!bat$).*$ The negative lookahead means: if the expression bat
doesn’t match at this point, try the rest of the pattern; if bat$ does
match, the whole pattern will fail. The trailing $ is required to ensure
that something like sample.batch, where the extension only starts with
bat, will be allowed.

Excluding another filename extension is now easy; simply add it as an
alternative inside the assertion. The following pattern excludes filenames that
end in either bat or exe:

The split() method of a pattern splits a string apart
wherever the RE matches, returning a list of the pieces. It’s similar to the
split() method of strings but provides much more generality in the
delimiters that you can split by; string split() only supports splitting by
whitespace or by a fixed string. As you’d expect, there’s a module-level
re.split() function, too.

.split(string[, maxsplit=0])

Split string by the matches of the regular expression. If capturing
parentheses are used in the RE, then their contents will also be returned as
part of the resulting list. If maxsplit is nonzero, at most maxsplit splits
are performed.

You can limit the number of splits made, by passing a value for maxsplit.
When maxsplit is nonzero, at most maxsplit splits will be made, and the
remainder of the string is returned as the final element of the list. In the
following example, the delimiter is any sequence of non-alphanumeric characters.

>>> p=re.compile(r'\W+')>>> p.split('This is a test, short and sweet, of split().')['This', 'is', 'a', 'test', 'short', 'and', 'sweet', 'of', 'split', '']>>> p.split('This is a test, short and sweet, of split().',3)['This', 'is', 'a', 'test, short and sweet, of split().']

Sometimes you’re not only interested in what the text between delimiters is, but
also need to know what the delimiter was. If capturing parentheses are used in
the RE, then their values are also returned as part of the list. Compare the
following calls:

Another common task is to find all the matches for a pattern, and replace them
with a different string. The sub() method takes a replacement value,
which can be either a string or a function, and the string to be processed.

.sub(replacement, string[, count=0])

Returns the string obtained by replacing the leftmost non-overlapping
occurrences of the RE in string by the replacement replacement. If the
pattern isn’t found, string is returned unchanged.

The optional argument count is the maximum number of pattern occurrences to be
replaced; count must be a non-negative integer. The default value of 0 means
to replace all occurrences.

Here’s a simple example of using the sub() method. It replaces colour
names with the word colour:

Empty matches are replaced only when they’re not adjacent to a previous match.

>>> p=re.compile('x*')>>> p.sub('-','abxd')'-a-b-d-'

If replacement is a string, any backslash escapes in it are processed. That
is, \n is converted to a single newline character, \r is converted to a
carriage return, and so forth. Unknown escapes such as \j are left alone.
Backreferences, such as \6, are replaced with the substring matched by the
corresponding group in the RE. This lets you incorporate portions of the
original text in the resulting replacement string.

This example matches the word section followed by a string enclosed in
{, }, and changes section to subsection:

There’s also a syntax for referring to named groups as defined by the
(?P<name>...) syntax. \g<name> will use the substring matched by the
group named name, and \g<number> uses the corresponding group number.
\g<2> is therefore equivalent to \2, but isn’t ambiguous in a
replacement string such as \g<2>0. (\20 would be interpreted as a
reference to group 20, not a reference to group 2 followed by the literal
character '0'.) The following substitutions are all equivalent, but use all
three variations of the replacement string.

replacement can also be a function, which gives you even more control. If
replacement is a function, the function is called for every non-overlapping
occurrence of pattern. On each call, the function is passed a
match object argument for the match and can use this
information to compute the desired replacement string and return it.

In the following example, the replacement function translates decimals into
hexadecimal:

When using the module-level re.sub() function, the pattern is passed as
the first argument. The pattern may be provided as an object or as a string; if
you need to specify regular expression flags, you must either use a
pattern object as the first parameter, or use embedded modifiers in the
pattern string, e.g. sub("(?i)b+","x","bbbbBBBB") returns 'xx'.

Regular expressions are a powerful tool for some applications, but in some ways
their behaviour isn’t intuitive and at times they don’t behave the way you may
expect them to. This section will point out some of the most common pitfalls.

Sometimes using the re module is a mistake. If you’re matching a fixed
string, or a single character class, and you’re not using any re features
such as the IGNORECASE flag, then the full power of regular expressions
may not be required. Strings have several methods for performing operations with
fixed strings and they’re usually much faster, because the implementation is a
single small C loop that’s been optimized for the purpose, instead of the large,
more generalized regular expression engine.

One example might be replacing a single fixed string with another one; for
example, you might replace word with deed. re.sub() seems like the
function to use for this, but consider the replace() method. Note that
replace() will also replace word inside words, turning swordfish
into sdeedfish, but the naive RE word would have done that, too. (To
avoid performing the substitution on parts of words, the pattern would have to
be \bword\b, in order to require that word have a word boundary on
either side. This takes the job beyond replace()‘s abilities.)

Another common task is deleting every occurrence of a single character from a
string or replacing it with another single character. You might do this with
something like re.sub('\n','',S), but translate() is capable of
doing both tasks and will be faster than any regular expression operation can
be.

In short, before turning to the re module, consider whether your problem
can be solved with a faster and simpler string method.

The match() function only checks if the RE matches at the beginning of the
string while search() will scan forward through the string for a match.
It’s important to keep this distinction in mind. Remember, match() will
only report a successful match which will start at 0; if the match wouldn’t
start at zero, match() will not report it.

Sometimes you’ll be tempted to keep using re.match(), and just add .*
to the front of your RE. Resist this temptation and use re.search()
instead. The regular expression compiler does some analysis of REs in order to
speed up the process of looking for a match. One such analysis figures out what
the first character of a match must be; for example, a pattern starting with
Crow must match starting with a 'C'. The analysis lets the engine
quickly scan through the string looking for the starting character, only trying
the full match if a 'C' is found.

Adding .* defeats this optimization, requiring scanning to the end of the
string and then backtracking to find a match for the rest of the RE. Use
re.search() instead.

When repeating a regular expression, as in a*, the resulting action is to
consume as much of the pattern as possible. This fact often bites you when
you’re trying to match a pair of balanced delimiters, such as the angle brackets
surrounding an HTML tag. The naive pattern for matching a single HTML tag
doesn’t work because of the greedy nature of .*.

The RE matches the '<' in <html>, and the .* consumes the rest of
the string. There’s still more left in the RE, though, and the > can’t
match at the end of the string, so the regular expression engine has to
backtrack character by character until it finds a match for the >. The
final match extends from the '<' in <html> to the '>' in
</title>, which isn’t what you want.

In this case, the solution is to use the non-greedy qualifiers *?, +?,
??, or {m,n}?, which match as little text as possible. In the above
example, the '>' is tried immediately after the first '<' matches, and
when it fails, the engine advances a character at a time, retrying the '>'
at every step. This produces just the right result:

>>> print(re.match('<.*?>',s).group())<html>

(Note that parsing HTML or XML with regular expressions is painful.
Quick-and-dirty patterns will handle common cases, but HTML and XML have special
cases that will break the obvious regular expression; by the time you’ve written
a regular expression that handles all of the possible cases, the patterns will
be very complicated. Use an HTML or XML parser module for such tasks.)

By now you’ve probably noticed that regular expressions are a very compact
notation, but they’re not terribly readable. REs of moderate complexity can
become lengthy collections of backslashes, parentheses, and metacharacters,
making them difficult to read and understand.

For such REs, specifying the re.VERBOSE flag when compiling the regular
expression can be helpful, because it allows you to format the regular
expression more clearly.

The re.VERBOSE flag has several effects. Whitespace in the regular
expression that isn’t inside a character class is ignored. This means that an
expression such as dog|cat is equivalent to the less readable dog|cat,
but [ab] will still match the characters 'a', 'b', or a space. In
addition, you can also put comments inside a RE; comments extend from a #
character to the next newline. When used with triple-quoted strings, this
enables REs to be formatted more neatly:

Regular expressions are a complicated topic. Did this document help you
understand them? Were there parts that were unclear, or Problems you
encountered that weren’t covered here? If so, please send suggestions for
improvements to the author.

The most complete book on regular expressions is almost certainly Jeffrey
Friedl’s Mastering Regular Expressions, published by O’Reilly. Unfortunately,
it exclusively concentrates on Perl and Java’s flavours of regular expressions,
and doesn’t contain any Python material at all, so it won’t be useful as a
reference for programming in Python. (The first edition covered Python’s
now-removed regex module, which won’t help you much.) Consider checking
it out from your library.